At 2024 LF Energy Summit, Abder Elandaloussi, a manager at Southern California Edison (SCE), delivered a session on the transformative potential of digital twins for electric utilities. With utilities facing mounting pressures from electrification, decarbonization, and grid complexity, Elandaloussi underscored the necessity of digital twins as a crucial tool for navigating these challenges. A summary of the session follows, with the full video at the end of this post.
Defining Digital Twins
Digital twins in the electric utility context represent a highly detailed, real-time, virtual replica of the grid’s physical components, systems, and their interactions. Elandaloussi clarified the confusion that often surrounds the term, explaining that many mistakenly label various systems like ADMS, DERMS, and DMS as digital twins. True digital twins go beyond these systems by integrating real-time data and predictive analytics to represent the grid in a multi-dimensional, high-fidelity manner, allowing utilities to simulate, monitor, and optimize their assets.
Drivers for Digital Twin Adoption
Elandaloussi highlighted several critical factors driving the adoption of digital twins. The electric grid is evolving rapidly due to the increasing penetration of renewable energy, electrification of transportation, and decentralization of generation through distributed energy resources (DERs). California’s ambitious 2045 decarbonization target was pointed out as a key milestone shaping the grid’s future. Despite these advancements, current utility planning, design, and operational processes have not kept pace with the unpredictability of grid behavior, prompting the need for innovative solutions like digital twins.
Core Components of Digital Twins
For digital twins to deliver value, Elandaloussi outlined several core components:
- Multi-domain simulation: Traditional single-dimension simulations fail to capture the interdependencies within the grid. Digital twins leverage both physics-based models and artificial intelligence (AI) to simulate complex interactions between grid elements in real-time.
- Contextual integration: Incorporating external factors—such as socio-economic, environmental, and topological data—into grid simulations is vital. These contextual elements help utilities make more informed decisions by understanding how external variables influence grid performance.
- Scalability and automation: A digital twin must be scalable and capable of evolving over time as the grid and its use cases expand. Automation is also essential to ensure real-time updates and reduce manual intervention.
Use Cases for Digital Twins
Elandaloussi presented several promising use cases for digital twins in the electric utility sector:
- Asset Management: Digital twins can monitor and predict the health and performance of grid assets like transformers, poles, and capacitor banks. By simulating wear and tear, utilities can anticipate failures and optimize maintenance schedules.
- Electrification of Transportation: With electric vehicles (EVs) growing in number, utilities need tools to understand the impacts of EVs on grid operations. Digital twins can model EV charging behaviors and assess the resulting grid strain, allowing for more efficient infrastructure planning.
- Cybersecurity and Wildfire Risk Management: Digital twins can simulate cyber-attacks or wildfire scenarios, helping utilities predict potential disruptions and design more resilient grids. For instance, they can model the socio-economic impacts of Public Safety Power Shutoffs (PSPS) and optimize responses.
- Model Validation and Consistency: Elandaloussi noted the importance of ensuring that planning, design, and operational models are aligned across the utility. Digital twins can act as a single source of truth, refining and validating models across departments, ensuring all teams operate with consistent assumptions.
SCE’s Digital Twin Initiative: MADES
SCE is pioneering an innovative digital twin project called Machine Learning Augmented Digital Simulation (MADES). The goal is to integrate operational and non-operational data to better reflect real-world conditions within power flow simulations. This initiative will improve the accuracy of simulations by incorporating real-time data and AI-driven insights, making the grid’s behavior more predictable and manageable.
Key Takeaways
In his conclusion, Elandaloussi emphasized that as utilities continue to collect vast amounts of data, the challenge will be to use that data effectively. The future grid will be a dynamic, multi-dimensional system that requires constant monitoring and adjustment. Digital twins, with their ability to simulate the grid in real time and predict future conditions, are set to become a foundational tool for utilities.
